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  * Easy and fast refinement at progressive resolutions   * Easy and fast refinement at progressive resolutions within a single project

EMAN2.2 Release Notes

This is not an all-inclusive list, it includes only the more interesting/useful changes since EMAN2.12

Single Particle Analysis

  • Many, deep improvements to refinement
    • Substantial refinement changes and new filtering techniques
    • Optional tophat filter (similar to Relion post processing), side chains often (but not always) look even better than Relion/CryoSparc
  • Local resolution and filtration
    • can be enabled in refinement to provide local detail appropriate to local resolution
  • Several new methods for conformational/compositional heterogeneity
    • Including multi-model refinement with or without alignment, masked particle subtraction, 2-D and 3-D Deep Learning approaches (experimental)
  • New bad particle identification strategy
    • Proven to produce better maps in several projects!
  • Automatic CTF
    • Used to be several manual steps. Entire process now automated.
    • Easy and fast refinement at progressive resolutions within a single project
  • New e2boxer (particle picker)
    • Fixes the problems with the old particle picker
    • New (optional) neural network picker for difficult projects
  • Stochastic Gradient Descent initial model generator (experimental)
  • Automatic magnification anisotropy correction tool
    • Post-processing program which corrects for the common microscope anisotropy problem on FEI scopes
    • Automatic, does not require additional data collection
  • New direct detector movie aligner
    • All new program. Competitive with other alignment programs in quality
    • Workflow for handling movies in EMAN2 projects

Subtomogram Averaging

  • New subtomogram averaging tools
    • New pipelines for subtomogram averaging and classification
  • Up to 20x faster 3-D alignments,
    • now practical to study 10,000 300x300x300 particles on a single workstation
  • New automatic missing wedge identification/compensation in alignment/averaging

Tomogram Segmentation

  • Workflow for semi-automatic tomogram annotation/segmentation
    • Uses convolutional neural network technology with user guided training of features.

Overall Changes

  • Anaconda Python based distribution
    • Integrates SciPy, Theano, PyLearn and other toolkits

  • New installers, with better OpenMPI/Pydusa support
  • GitHub

    • Source code is now managed via a public GitHub repository (cryoem/eman2)

  • Windows 10/7 64 bit
    • Initial support, poorly tested, but available for the first time (EMAN2 only, SPARX/SPHIRE do not support this platform)

EMAN2/Eman22Release (last edited 2021-03-31 03:45:14 by SteveLudtke)